A Decision Tree Induction Algorithm for Efficient Rule Evaluation Using Shannon’s Expansion

Conference paper (2023)

Authors

Vitali Herrera-Semenets Advanced Technologies Application Center
Lázaro Bustio-Martínez Iberoamericana University
Raudel Hernández-León Advanced Technologies Application Center
Jan van den Berg Cyber Security - EEMCS
Research Group
Cyber Security (EEMCS) (TU Delft)
Copyright: © 2023 Vitali Herrera-Semenets, Lázaro Bustio-Martínez, Raudel Hernández-León, Jan van den Berg
DOI
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https://doi.org/10.1007/978-3-031-47765-2_18
Decision Tree Data Processing Rule-Based Systems
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

Abstract

Decision trees are one of the most popular structures for decision-making and the representation of a set of rules. However, when a rule set is represented as a decision tree, some quirks in its structure may negatively affect its performance. For example, duplicate sub-trees and rule filters, that need to be evaluated more than once, could negatively affect the efficiency. This paper presents a novel algorithm based on Shannon’s expansion, which guarantees that the same rule filter is not evaluated more than once, even if repeated in other rules. This fact increases efficiency during the evaluation process using the induced decision tree. Experiments demonstrated the viability of the proposed algorithm in processing-intensive scenarios, such as in intrusion detection and data stream analysis.